Fraud Detection AI in India

Fraud teams at Indian fintechs, NBFCs, and marketplaces increasingly use large language models alongside their existing rules engines and ML scoring — not to replace them, but to read the free text a rules engine can't: inconsistent loan-application narratives, suspicious support chat, or messy transaction descriptions. unoblox gives these teams one rupee-billed API to call multiple models for that layer of work, budgeted the way an Indian finance team actually plans: per million tokens, in rupees.

Where an LLM fits in a fraud stack

An LLM is not a fraud-scoring model by itself — it doesn't replace your transaction-velocity rules or your trained classifier. What it's genuinely useful for is reasoning over unstructured text: flagging when an application's stated purpose doesn't match its supporting documents, summarising a suspicious case for a human investigator, or extracting structured fields from a free-text complaint so your existing systems can act on it.

Two tiers: real-time flags vs investigation summaries

Use	Suggested model	₹ input / ₹ output (per 1M tokens)
High-volume, low-latency text flagging	deepseek-ai/deepseek-v4-flash	₹9.07 / ₹18.14
Bulk narrative classification	qwen/qwen3-235b-a22b-instruct-2507	₹9.07 / ₹55.44
Investigator-facing case summaries	openai/gpt-5	₹126 / ₹1008
Longer-form case writeups	anthropic/claude-sonnet-4-5	₹201.6 / ₹1008
Prototyping your prompts	qwen/qwen3-1.7b	FREE (₹0)

Run the cheap, fast model on every case and reserve the expensive one for cases that actually get escalated — that split is where most of the cost control happens.

A sample flagging call

curl https://api.unoblox.ai/v1/chat/completions \
  -H "Authorization: Bearer ub-gw-xxxxxxxxxxxxxxxx" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-ai/deepseek-v4-flash",
    "messages": [{"role": "user", "content": "Does this loan application note contain any internal inconsistency? Answer yes/no and explain in one line."}]
  }'

Compliance is your responsibility, not a feature we sell

unoblox is infrastructure — an API and a rupee invoice — not a compliance certification or a regulatory product. You control what data you send in a prompt, and you should involve your own compliance and legal teams on data handling, retention, and audit requirements for fraud workflows. We won't claim a regulatory status we don't have, and you shouldn't infer one either.

Frequently asked questions

Can an LLM replace our fraud-scoring model? No. It's a complement for unstructured text reasoning, not a replacement for a trained classifier or rules engine.

Which model should run on every transaction? Whichever cheap, fast model performs acceptably on your test set — deepseek-ai/deepseek-v4-flash is the lowest-cost paid option in the catalog, which makes it a natural first candidate.

Can I test before committing spend? Yes — qwen/qwen3-1.7b is free, so you can validate your prompt logic before paying for a larger model.

Does unoblox store the data I send? unoblox is a gateway to the underlying model providers; treat any prompt content the way you would treat sending it to that provider, and route sensitive data accordingly. Discuss retention specifics with your unoblox account contact before sending regulated data.

Is billing really in rupees with GST? Yes — every model is priced per million tokens in rupees, and you receive a monthly GST invoice.

Do the foreign models keep data in India? No — GPT, Claude, DeepSeek, and Qwen models run on their providers' infrastructure outside India. unoblox's India benefit here is rupee billing and one endpoint, not data residency.

Get started in rupees → https://unoblox.ai/sign-in